Tian et al. 2011 Model 4 (G2PDeep Inspired, Smaller)

This model is based on the previous G2PDeep inspired model but differs as follows. The previous model used strides of length 1. This results in a huge model that's difficult to train (batch size of 16 is sufficient to overload the 8GB limit). Here the goal is to have a performant model that's smaller and possibly deeper.

# Run Settings:
nb_name = '14_TianEtAl2011'# Set manually! -----------------------------------

downsample_obs = True
train_n = 90
test_n = 10

dataloader_batch_size = 8 #16 #64
run_epochs = 2

use_gpu_num = 1

# Imports --------------------------------------------------------------------
import os
import pandas as pd
import numpy as np
import re

import torch
from torch.utils.data import Dataset
from torch.utils.data import DataLoader
from torch import nn

import tqdm
from tqdm import tqdm

import plotly.graph_objects as go
import plotly.express as px
import plotly.io as pio
pio.templates.default = "plotly_white"


import dlgwas
from dlgwas.kegg import ensure_dir_path_exists
from dlgwas.kegg import get_cached_result
from dlgwas.kegg import put_cached_result

from dlgwas.dlfn import calc_cs
from dlgwas.dlfn import apply_cs
from dlgwas.dlfn import reverse_cs

from dlgwas.dlfn import TianEtAl2011Dataset
from dlgwas.dlfn import train_loop
from dlgwas.dlfn import train_error
from dlgwas.dlfn import test_loop
from dlgwas.dlfn import train_nn
from dlgwas.dlfn import yhat_loop


device = "cuda" if torch.cuda.is_available() else "cpu"
if use_gpu_num in [0, 1]: 
    torch.cuda.set_device(use_gpu_num)
print(f"Using {device} device")


ensure_dir_path_exists(dir_path = '../models/'+nb_name)
ensure_dir_path_exists(dir_path = '../reports/'+nb_name)

ensure_dir_path_exists(dir_path = '../models/'+nb_name)
ensure_dir_path_exists(dir_path = '../reports/'+nb_name)

/home/labmember/mambaforge/envs/pytorch_mamba/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
  from .autonotebook import tqdm as notebook_tqdm

Using cuda device

Load Cleaned Data

# Read in cleaned data
taxa_groupings = pd.read_csv('../models/10_TianEtAl2011/taxa_groupings.csv')
data           = pd.read_csv('../models/10_TianEtAl2011/clean_data.csv')

# Define holdout sets (Populations)
uniq_pop = list(set(taxa_groupings['Population']))
print(str(len(uniq_pop))+" Unique Holdout Groups.")
taxa_groupings['Holdout'] = None
for i in range(len(uniq_pop)):
    mask = (taxa_groupings['Population'] == uniq_pop[i])
    taxa_groupings.loc[mask, 'Holdout'] = i

taxa_groupings

25 Unique Holdout Groups.

	Unnamed: 0	sample	Population	Holdout
0	0	Z001E0001	B73 x B97	0
1	1	Z001E0002	B73 x B97	0
2	2	Z001E0003	B73 x B97	0
3	3	Z001E0004	B73 x B97	0
4	4	Z001E0005	B73 x B97	0
...	...	...	...	...
4671	4671	Z026E0196	B73 x Tzi8	8
4672	4672	Z026E0197	B73 x Tzi8	8
4673	4673	Z026E0198	B73 x Tzi8	8
4674	4674	Z026E0199	B73 x Tzi8	8
4675	4675	Z026E0200	B73 x Tzi8	8

4676 rows × 4 columns

Setup Holdouts

#randomly holdout a population if there is not a file with the population held out.
# Holdout_Int = 0
Holdout_Int_path = '../models/'+nb_name+'/holdout_pop_int.pkl'
if None != get_cached_result(Holdout_Int_path):
    Holdout_Int = get_cached_result(Holdout_Int_path)
else:
    Holdout_Int = int(np.random.choice([i for i in range(len(uniq_pop))], 1))
    put_cached_result(Holdout_Int_path, Holdout_Int)

    
print("Holding out i="+str(Holdout_Int)+": "+uniq_pop[Holdout_Int])

mask = (taxa_groupings['Holdout'] == Holdout_Int)
train_idxs = list(taxa_groupings.loc[~mask, ].index)
test_idxs = list(taxa_groupings.loc[mask, ].index)

Holding out i=13: B73 x NC358

# downsample_obs = True
# train_n = 900
# test_n = 100

if downsample_obs == True:
    train_idxs = np.random.choice(train_idxs, train_n)
    test_idxs = np.random.choice(test_idxs, test_n)
    print([len(e) for e in [test_idxs, train_idxs]])
    
# used to go from index in tensor to index in data so that the right xs tensor can be loaded in
idx_original = np.array(data.index)

y1 = data['leaf_length']
y2 = data['leaf_width']
y3 = data['upper_leaf_angle']
y1 = np.array(y1)
y2 = np.array(y2)
y3 = np.array(y3)

[10, 90]

Scale data

scale_dict_path = '../models/'+nb_name+'/scale_dict.pkl'
if None != get_cached_result(scale_dict_path):
    scale_dict = get_cached_result(scale_dict_path)
else:
    scale_dict = {
        'y1':calc_cs(y1[train_idxs]),
        'y2':calc_cs(y2[train_idxs]),
        'y3':calc_cs(y3[train_idxs])
    }
    put_cached_result(scale_dict_path, scale_dict)

y1 = apply_cs(y1, scale_dict['y1'])
y2 = apply_cs(y2, scale_dict['y2'])
y3 = apply_cs(y3, scale_dict['y3'])

Allow for cycling data onto and off of GPU

# loading this into memory causes the session to crash

y1_train = torch.from_numpy(y1[train_idxs])[:, None]
y2_train = torch.from_numpy(y2[train_idxs])[:, None]
y3_train = torch.from_numpy(y3[train_idxs])[:, None]

idx_original_train = torch.from_numpy(idx_original[train_idxs])

y1_test = torch.from_numpy(y1[test_idxs])[:, None]
y2_test = torch.from_numpy(y2[test_idxs])[:, None]
y3_test = torch.from_numpy(y3[test_idxs])[:, None]

idx_original_test = torch.from_numpy(idx_original[test_idxs])


# dataloader_batch_size = 64

training_dataloader = DataLoader(
    TianEtAl2011Dataset(
        y1 = y1_train,
        y2 = y2_train,
        y3 = y3_train,
        idx_original = idx_original_train,
        use_gpu_num = use_gpu_num,
#         device = 'cpu'
    ), 
    batch_size = dataloader_batch_size, 
    shuffle = True)

testing_dataloader = DataLoader(
    TianEtAl2011Dataset(
        y1 = y1_test,
        y2 = y2_test,
        y3 = y3_test,
        idx_original = idx_original_train,
        use_gpu_num = use_gpu_num,
#         device = 'cpu'
    ), 
    batch_size = dataloader_batch_size, 
    shuffle = True)

Using cuda device
Using cuda device

Non-Boilerplate

class NeuralNetwork(nn.Module):
    def __init__(self):
        super(NeuralNetwork, self).__init__()    

        # Block 1 ------------------------------------------------------------
        self.long_way_0 = nn.Sequential(
            nn.Conv1d(
                    in_channels= 4, # second channel
                    out_channels= 4,
                    kernel_size= 3,
                    stride= 2,
                    bias = True
                ),
#             nn.BatchNorm1d(4),
            nn.Conv1d(
                    in_channels= 4, 
                    out_channels= 4,
                    kernel_size= 3,
                    stride= 1,
                    padding = 1,
                    bias = True
                ),
#             nn.BatchNorm1d(4),
            nn.Dropout(p=0.75)
            )
        
        self.shortcut_0 = nn.Sequential(
            nn.Conv1d(
                    in_channels= 4, 
                    out_channels= 4,
                    kernel_size= 3,
                    stride= 2,
                    bias = True
                )
        )
        # Block 2 ------------------------------------------------------------
        self.long_way_1 = nn.Sequential(
            nn.Conv1d(
                    in_channels= 4, # second channel
                    out_channels= 4,
                    kernel_size= 3,
                    stride= 2,
                    bias = True
                ),
#             nn.BatchNorm1d(4),
            nn.Conv1d(
                    in_channels= 4, 
                    out_channels= 4,
                    kernel_size= 3,
                    stride= 1,
                    padding = 1,
                    bias = True
                ),
#             nn.BatchNorm1d(4),
            nn.Dropout(p=0.75)
            )
        
        self.shortcut_1 = nn.Sequential(
            nn.Conv1d(
                    in_channels= 4, 
                    out_channels= 4,
                    kernel_size= 3,
                    stride= 2,
                    bias = True
                )
        )
        # Block 3 ------------------------------------------------------------
        self.long_way_2 = nn.Sequential(
            nn.Conv1d(
                    in_channels= 4, # second channel
                    out_channels= 4,
                    kernel_size= 3,
                    stride= 2,
                    bias = True
                ),
#             nn.BatchNorm1d(4),
            nn.Conv1d(
                    in_channels= 4, 
                    out_channels= 4,
                    kernel_size= 3,
                    stride= 1,
                    padding = 1,
                    bias = True
                ),
#             nn.BatchNorm1d(4),
            nn.Dropout(p=0.75)
            )
        
        self.shortcut_2 = nn.Sequential(
            nn.Conv1d(
                    in_channels= 4, 
                    out_channels= 4,
                    kernel_size= 3,
                    stride= 2,
                    bias = True
                )
        )        
        
        
        self.feature_processing = nn.Sequential(
            nn.Conv1d(
                    in_channels= 4, 
                    out_channels= 4,
                    kernel_size= 3,
                    stride = 2,
                    bias = True
                ),
            nn.Dropout(p=0.75)
        )

        self.output_processing = nn.Sequential(
            nn.Flatten(),
            nn.ReLU(), # They used inverse square root activation $y = \frac{x}{\sqrt{1+ax^2}}$
            nn.Dropout(p=0.75),
            nn.Linear(235860, 1)
        )            
        
    def forward(self, x):
        x_out = self.long_way_0(x)
        x_shortcut = self.shortcut_0(x)
        x_out += x_shortcut
        
        x = x_out
        x_out = self.long_way_1(x)
        x_shortcut = self.shortcut_1(x)
        x_out += x_shortcut
        
        x = x_out
        x_out = self.long_way_2(x)
        x_shortcut = self.shortcut_2(x)        
        x_out += x_shortcut
        
        x_out = self.feature_processing(x_out)
        x_out = self.output_processing(x_out)
        
        return x_out
    
# model = NeuralNetwork().to(device)

# res = model(xs_i) # try prediction on one batch
# res.shape

def train_nn(
    nb_name,
    training_dataloader,
    testing_dataloader,
    model,
    learning_rate = 1e-3,
    batch_size = 64,
    epochs = 500
):
    # Initialize the loss function
    loss_fn = nn.MSELoss()
#     optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)

    # Optimizer with L2 normalization
    optimizer = torch.optim.Adam([
        {'params':model.long_way_0.parameters(), 'weight_decay': 0.1},
        {'params':model.shortcut_0.parameters(), 'weight_decay': 0.1},
        {'params':model.long_way_1.parameters(), 'weight_decay': 0.1},
        {'params':model.shortcut_1.parameters(), 'weight_decay': 0.1},
        {'params':model.long_way_2.parameters(), 'weight_decay': 0.1},
        {'params':model.shortcut_2.parameters(), 'weight_decay': 0.1},
        {'params':model.feature_processing.parameters(),        'weight_decay': 0.1},
        {'params':model.output_processing.parameters(),         'weight_decay': 0.01},
    ], lr=learning_rate)

    loss_df = pd.DataFrame([i for i in range(epochs)], columns = ['Epoch'])
    loss_df['TrainMSE'] = np.nan
    loss_df['TestMSE']  = np.nan

    for t in tqdm(range(epochs)):        
#         print(f"Epoch {t+1}\n-------------------------------")
        train_loop(training_dataloader, model, loss_fn, optimizer, silent = True)

        loss_df.loc[loss_df.index == t, 'TrainMSE'
                   ] = train_error(training_dataloader, model, loss_fn, silent = True)
        
        loss_df.loc[loss_df.index == t, 'TestMSE'
                   ] = test_loop(testing_dataloader, model, loss_fn, silent = True)
        
        if (t+1)%10: # Cache in case training is interupted
#             print(loss_df.loc[loss_df.index == t, ['TrainMSE', 'TestMSE']])
            torch.save(model.state_dict(), 
                       '../models/'+nb_name+'/model_'+str(t)+'_'+str(epochs)+'.pt') # convention is to use .pt or .pth
            loss_df.to_csv('../reports/'+nb_name+'/loss_df'+str(t)+'_'+str(epochs)+'.csv', index=False) 
        
    return([model, loss_df])

# don't run if either of these exist because there may be cases where we want the results but not the model

if not os.path.exists('../models/'+nb_name+'/model.pt'): 
    model = NeuralNetwork().to(device)    

    model, loss_df = train_nn(
        nb_name,
        training_dataloader,
        testing_dataloader,
        model,
        learning_rate = 1e-3,
        batch_size = dataloader_batch_size,
        epochs = run_epochs
    )
    
    # experimental outputs:
    # 1. Model
    torch.save(model.state_dict(), '../models/'+nb_name+'/model.pt') # convention is to use .pt or .pth

    # 2. loss_df
    loss_df.to_csv('../reports/'+nb_name+'/loss_df.csv', index=False)  
    
    
    # 3. predictions 
    yhats = pd.concat([
        yhat_loop(testing_dataloader, model).assign(Split = 'Test'),
        yhat_loop(training_dataloader, model).assign(Split = 'Train')], axis = 0)

    yhats.to_csv('../reports/'+nb_name+'/yhats.csv', index=False)

100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:14<00:00,  7.08s/it]

NeuralNetwork()

NeuralNetwork(
  (long_way_0): Sequential(
    (0): Conv1d(4, 4, kernel_size=(3,), stride=(2,))
    (1): Conv1d(4, 4, kernel_size=(3,), stride=(1,), padding=(1,))
    (2): Dropout(p=0.75, inplace=False)
  )
  (shortcut_0): Sequential(
    (0): Conv1d(4, 4, kernel_size=(3,), stride=(2,))
  )
  (long_way_1): Sequential(
    (0): Conv1d(4, 4, kernel_size=(3,), stride=(2,))
    (1): Conv1d(4, 4, kernel_size=(3,), stride=(1,), padding=(1,))
    (2): Dropout(p=0.75, inplace=False)
  )
  (shortcut_1): Sequential(
    (0): Conv1d(4, 4, kernel_size=(3,), stride=(2,))
  )
  (long_way_2): Sequential(
    (0): Conv1d(4, 4, kernel_size=(3,), stride=(2,))
    (1): Conv1d(4, 4, kernel_size=(3,), stride=(1,), padding=(1,))
    (2): Dropout(p=0.75, inplace=False)
  )
  (shortcut_2): Sequential(
    (0): Conv1d(4, 4, kernel_size=(3,), stride=(2,))
  )
  (feature_processing): Sequential(
    (0): Conv1d(4, 4, kernel_size=(3,), stride=(2,))
    (1): Dropout(p=0.75, inplace=False)
  )
  (output_processing): Sequential(
    (0): Flatten(start_dim=1, end_dim=-1)
    (1): ReLU()
    (2): Dropout(p=0.75, inplace=False)
    (3): Linear(in_features=235860, out_features=1, bias=True)
  )
)

Standard Visualizations

loss_df = pd.read_csv('../reports/'+nb_name+'/loss_df.csv')

loss_df.TrainMSE = reverse_cs(loss_df.TrainMSE, scale_dict['y1'])
loss_df.TestMSE  = reverse_cs(loss_df.TestMSE , scale_dict['y1'])


fig = go.Figure()
fig.add_trace(go.Scatter(x=loss_df.Epoch, y=loss_df.TestMSE,
                    mode='lines', name='Test'))
fig.add_trace(go.Scatter(x=loss_df.Epoch, y=loss_df.TrainMSE,
                    mode='lines', name='Train'))
fig.show()

yhats = pd.read_csv('../reports/'+nb_name+'/yhats.csv')

yhats.y_true = reverse_cs(yhats.y_true, scale_dict['y1'])
yhats.y_pred = reverse_cs(yhats.y_pred, scale_dict['y1'])

px.scatter(yhats, x = 'y_true', y = 'y_pred', color = 'Split', trendline="ols")

yhats['Error'] = yhats.y_true - yhats.y_pred

px.histogram(yhats, x = 'Error', color = 'Split',
             marginal="box", # can be `rug`, `violin`
             nbins= 50)